Housing Assistance and Labor Supply: The Case of Rent Control

Rental housing in urban areas across the U.S. has become increasingly unaffordable. In response, legislative momentum for rent control has grown, restricting rent growth in many local municipalities. Although economists generally oppose such regulations for distorting the housing market and creating significant efficiency costs, activists argue that they promote stability, equality, and social justice. Existing research on rent control has focused primarily on its direct effect on housing; little is known about potential spillover effects on tenant outcomes. Recognizing the close link between housing and labor markets, this project investigates whether rent control influences tenant labor supply, using high-quality micro-data on rent stabilization in New York City and employing rigorous statistical methods. Specifically, we ask: Does rent control increase tenants’ labor supply by fostering residential stability? Or does it reduce labor supply through an implicit rent subsidy (an income effect)? Which demographic groups of tenants are most affected? Answers to these questions will inform both the effectiveness and equity of rent control policies and deepen our understanding of how housing assistance shapes labor market outcomes more broadly

Implicit Insurance in the United States

A long literature in labor economics studies the causal effect of job loss on workers’ earnings trajectories, finding substantial and persistent long-run reductions in wage income after displacement (Jacobson et al., 1993; Couch and Placzek, 2010). However, this work has generally not studied the net effects of the tax system, government transfers, and self-insurance in mitigating these earnings losses in a single, unified dataset. For example, workers may receive transfer payments, implying smaller total income losses relative to wage losses. Households will exhibit net behavioral responses to job loss: workers may liquidate assets, enter self-employment, reduce reported income, or increase other family members’ labor market activity to further mitigate household income losses relative to worker wage losses. Progressive tax rates and tax credits aimed at low-income families might result in smaller after-tax income losses relative to pre-tax effects. To what extent do each of these factors provide implicit insurance against wage losses? To make progress on this question, we leverage a “mass layoff” design to uncover job losses that were caused by firm-level downsizing. Specifically, by using large worker flows in employer-employee linked data to identify large-scale displacement events, we can isolate workers for whom job loss is plausibly exogenous. This research design is commonly used for identification in other analyses of job displacement (Jacobson et al., 1993; Couch and Placzek, 2010; Sullivan and von Wachter, 2009). To jointly examine changes in net household resources following job loss, we will utilize a series of novel linkages between newly available administrative datasets at the United States Census Bureau. We first estimate the treatment effect of job loss on individual worker labor market outcomes, including wage employment, independent contracting entry, and total labor earnings. We supplement these outcomes with estimates of effects on public transfer receipt, including: unemployment insurance (UI), the Supplemental Nutrition Assistance Program (SNAP), Social Security, Social Security Disability Insurance (SSDI), Supplemental Security Income (SSI), Temporary Aid for Needy Families (TANF), housing assistance, and the Women, Infants, and Children (WIC) program. Next, we move to analyzing responses at the household or tax filing unit level, including changes in taxes paid, credits received, and spousal earnings responses. The prior literature studying these responses suffers from important data challenges, which our study aims to address. For example, many of our outcomes are generally underreported or unobserved in other datasets, and previous research has employed alternate empirical approaches, restricted samples to smaller subpopulations, or has been underpowered to examine heterogeneity in treatment effects due to sample size

Assessing the Labor Market Consequences of Geographic Shifts Driven by Remote Worker Relocation Programs

Since 2018, over 80 U.S. municipalities have introduced Remote Worker Relocation Programs to attract remote workers and revitalize local economies. Despite their growing popularity, their broader labor market impacts, including potential spillover effects on non-participating regions, remain unclear. This project will investigate who these relocated remote workers are, along with their location elasticity estimates with respect to subsidies, and the broader economic consequences of these remote workers’ geographic reallocation. Leveraging data from MakeMyMove—a unique platform connecting remote workers with communities—and People Data Lab, it will employ a staggered difference-in-differences approach and build a theoretical framework incorporating agglomeration and congestion forces. The findings will offer insights into how these programs shape economic efficiency and equity, guiding policymakers